Translational Vision Science and Technology

Cities are important carriers of green innovation. The foundation for accelerating China’s ecological civilization construction and fostering regionally coordinated and sustainable development is quantitative analysis of the spatial evolution pattern and influencing factors of urban green innovation, as well as revealing the development differences between regions. This study’s research object includes 284 Chinese cities that are at the prefecture level or above, excluding Xizang, Hong Kong, Macao, and Taiwan of China due to incomplete data. The spatial evolution characteristics of urban green innovation in China between 2005 and 2021 are comprehensively described using the gravity center model and boxplot analysis. The factors that affect urban green innovation are examined using the spatial Durbin model (SDM). The findings indicate that: 1) over the period of the study, the gravity center of urban green innovation in China has always been distributed in the Henan-Anhui border region, showing a migration characteristic of “initially shifting northeast, subsequently southeast”, and the migration speed has gradually increased. 2) Although there are also noticeable disparities in east-west, the north-south gap is the main cause of the shift in China’s urban green innovation gravity center. The primary areas of urban green innovation in China are the cities with green innovation levels higher than the median. 3) The main influencing factor of urban green innovation is the industrial structure level. The effect of the financial development level, the government intervention level, and the openness to the outside world degree on urban green innovation is weakened in turn. The environmental regulation degree is not truly influencing urban green innovation. The impact of various factors on green innovation across cities of different sizes, exhibiting heterogeneity. This study is conducive to broadening the academic community’s comprehension of the spatial evolution characteristics of urban green innovation and offering a theoretical framework for developing policies for the all-encompassing green transformation of social and economic growth.

Agricultural drought, a prolonged disaster with widespread impacts, exerts tremendous pressure on farm household activities, agricultural production, and economic development. The western region of Jilin Province, China, located in a semi-arid zone, where persistents drought exacerbates ecological fragility. With a larger proportion of its population living in rural areas, the life and production activities of households are particularly susceptible to drought-related challenges. This study focuses on the western part of Jilin Province, utilizing remote sensing data to calculate the temperature vegetation dryness index (TVDI) as a soil moisture monitoring indicator for drought assessment. The livelihood vulnerability index (LVI) and the improved LVI-Intergovernmental Panel on Climate Change (IPCC) index are employed to comprehensively assess the livelihood vulnerability of communes in the region and the impact of drought conditions on farmers’ livelihood vulnerability. The results reveal a spatial trend of increasing drought severity from northeast to southwest, with temporally minor fluctuations observed in drought levels from 2005 to 2022. Livelihood vulnerability results indicate significant spatial and temporal variations, with education, health, food, and water playing key roles. Correlation analysis indicates a strong relationship between TVDI and LVI, highlighting the detrimental impact of drought on farmers’ livelihoods. The study aims to provide a scientific foundation for managing livelihood vulnerability in the western part of Jilin Province and similar arid areas. Additionally, it seeks to offer strategic recommendations for policymakers to mitigate the adverse effects of drought, thereby reducing farmers’ vulnerability and fostering sustainable socio-economic development.

Climate change caused by carbon emissions is a hot topic of concern. Enhancing carbon emission performance (CEP) emerges as a pivotal strategy to curtail carbon emissions, with the digital economy recognized as a crucial instrument for bolstering CEP. Grounded in theoretical analysis, this article takes the Yangtze River Delta region (YRD) as the research object and conducts empirical analysis for the period from 2010 to 2021. The Super Epsilon-Based Measure (EBM) model was employed to assess CEP, while the entropy method was used to quantify the level of the digital economy. Baseline regression models and mediation effect models were constructed to test the research hypotheses. Additionally, the Spatial Durbin Model (SDM) was utilized to analyze the spatial spillover effects of the digital economy. Some conclusions were drawn as follows. Firstly, both the digital economy and CEP exhibit growing trends and demonstrate significant spatial distribution characteristics. Cities with high CEP are increasingly concentrated along the Yangtze River and coastal areas. Meanwhile, the digital economy generally demonstrates a spatial distribution pattern of being higher in the southeast and lower in the northwest. Secondly, the digital economy exerts a notable and consistent positive influence on CEP, but this impact is not primarily achieved through promoting green technology innovation. Instead, the digital economy exhibits a stronger intermediary effect on CEP by facilitating industrial structure upgrading and rationalization. Thirdly, the digital economy significantly enhancing local CEP but having an insignificant impact on neighboring cities’ CEP. To address these findings, cities ought to invest in digital infrastructure, incentivize digital innovation through policy and financial backing, and harness advanced technologies like 5G and blockchain to promote low-carbon, intelligent production and lifestyles, while enhancing industrial structure and regional cooperation to foster a low-carbon digital economy network.

Quantifying and mapping how ecosystem services impact agricultural competitiveness is crucial for attaining the Sustainable Development Goals of United Nations. However, few study quantified agricultural competitiveness and mapped the effects of ecosystem services on agricultural competitiveness using multiple models. In this study, multi-source data from 2000 to 2020 were utilized to establish the indicator system of agricultural competitiveness; five ecosystem services were quantified using computation models; Geographic Information System (GIS) spatial analysis was used to explore the spatial patterns of agricultural competitiveness and ecosystem services; geographic detector models were applied to investigate the effects and driving mechanisms of ecosystem services on agricultural competitiveness. Shandong Province of China was selected as the case study area. The results demonstrated that: 1) there was a significant increase in agricultural competitiveness during the study period, with high levels observed mainly in the east region of the study area. 2) The spatial distribution patterns of ecosystem services and agricultural competitiveness primarily exhibited High-High and Low-Low Cluster types. 3) Habitat quality emerged as the main driving factor of agricultural competitiveness in 2000 and 2020, while water yield played a substantial role in 2010. 4) The coupling of two ecosystem services exerted a greater effect on agricultural competitiveness compared to individual ecosystem service. The innovations of this study are constructing an indicator system to quantify agricultural competitiveness, and exploring the effects of ecosystem services on agricultural competitiveness. This study proposed an indicator system to quantify agricultural competitiveness, which can be applied in other regions, and explored the effects of ecosystem services on agricultural competitiveness. The findings of this study can serve as valuable insights for policymakers to formulate tailored agricultural development policies that take into account the synergistic effects of ecosystem services on agricultural competitiveness.

Intercity mobility lays the foundation for capital flow, information flow, and knowledge flow, etc., and is important for promoting regional integration. Although many scholars have studied intercity mobility in extensive well-developed urban agglomerations, few studies have examined the characteristics of intercity mobility at the county level and its impact on regionalization in western China. This study takes the Guanzhong Plain urban agglomeration (GPUA) as a case to study the geographical law of intercity mobility and then explore its impact on regionalization. The results obtained show that intercity mobility network exhibits a hub and spoke patterns focusing on major municipal districts at the county level. We also found a corridor effect that counties with higher travel volumes are mostly located along the trunk high speed railway (HSR) lines. Unlike previous studies, the distribution of intercity mobility is more concentrated than that of population and exhibits a super-linear behavior rule. There are the differences in gravity law for overall trips, weekday trips, weekend trips, and holiday trips. With the decrease of travel duration, the effect of attraction of destination is weakening, but the influence of distance decay is increasing. Finally, the spatial organization is still administrative-centric and is dominated by intraprefecture and intra-provincial development. Moreover, the coupled degree between network-based regionalization and attribute-based regionalization shows a decreasing trend from administrative via cultural to physical factors. These findings enrich the research on the intercity mobility and the regionalization in inland developing urban agglomerations.

Water scarcity and soil salinization pose significant challenges to agriculture in the West Liaohe Plain, eastern Inner Mongolia, China. Shallow-buried drip irrigation can improve soil water use efficiency to alleviate water shortage in agriculture and the application of lignite humic acid reduces the adverse effects of soil salinization. However, further research is needed to investigate the effects of different application rates of lignite humic acid and humic acid-based combined amendment on soil physicochemical properties, nutrient contents, and crop yield in saline-sodic farmlands under shallow-buried drip irrigation. A two-year field experiment was conducted with control without any amendment (CK), three treatments amended with 3 t/ha (H1), 6 t/ha (H2), and 12 t/ha (H3) lignite humic acid, and three application rates with 15 t/ha (T1), 22.5 t/ha (T2), and 30 t/ha (T3) lignite humic acid-based combined amendment in 2021 and 2022. The results showed that H3 reduced soil bulk density, pH, electrical conductivity, and total alkalinity, while increasing the contents of soil organic matter, total nitrogen, and available potassium in the two-year experiment. Moreover, the maize yield in H3 increased by an average of 35.5%. T2 decreased soil bulk density, pH, total alkalinity, and increased maize yield by 16.2%, compared to the first year. These results suggest that T2 consistently improved both soil quality and crop yield. Correlation analyses showed that lignite humic acid and its complexes promote maize growth and increase yield by increasing soil organic matter and total nitrogen while reducing soil salinity and total alkalinity. Based on the comprehensive analysis of the field data and the results of the comprehensive evaluation of soil quality, it was determined that the appropriate improvement measures for saline-sodic farmlands under shallow-buried drip irrigation are the application of 12 t/ha of lignite humic acid and 22.5 t/ha of lignite humic acid-based combined amendment. This study demonstrates the effectiveness of lignite humic acid and its combined amendment in mitigating the constraints of saline-sodic farmlands and enhancing crop yields, providing a sustainable solution for improving saline-sodic farmlands in the West Liaohe Plain.

Investigating the ecological impact of land use change in the context of the construction of national water network project is crucial, as it is imperative for achieving the sustainable development goals of the national water network and guaranteeing regional ecological stability. Using the Danjiangkou Reservoir Area (DRA), China as the study area, this paper first examined the spatiotemporal dynamics of natural landscape patterns and ecosystem service values (ESV) in the DRA from 2000 to 2018 and then investigated the spatial clustering characteristics of the ESV using spatial statistical analysis tools. Finally, the patch-generating land use simulation (PLUS) model was used to simulate the natural landscape and future changes in the ESV of the DRA from 2018 to 2028 under four different development scenarios: business as usual (BAU), economic development (ED), ecological protection (EP), and shoreline protection (SP). The results show that: during 2000–2018, the construction of water facilities had a significant impact on regional land use/land cover (LULC) change, with a 24 830 ha increase in watershed area. ESV exhibited an increasing trend, with a significant and growing spatial clustering effect. The transformation of farmland to water bodies led to accelerated ESV growth, while the transformation of forest land to farmland led to a decrease in the ESV. Normalized difference vegetation index (NDVI) had the strongest effect on the ESV. ESV exhibited a continuous increase from 2018 to 2028 under all the simulation scenarios. The EP scenario had the greatest increase in ESV, while the ED scenario had the smallest increase. The findings suggest that projected land use patterns under different scenarios have varied impacts on ecosystem services (ESs) and that the management and planning of the DRA should balance social, economic, ecological, and security benefits.nomic, ecological, and security benefits.

The Yangtze River economic belt (YREB), China is important to the Chinese economy and for supporting sustainable development. Clarifying the relationship between water quality indices and socioeconomic indicators could help improve aquatic environment management in the YREB and our understanding of the causes and effects of water quality variations in other large river basins. In this study, river water quality, factors affecting water quality, and management strategies, and correlations between water quality indices and socioeconomic indicators in the YREB during the 13th Five-Year Plan period (2016–2020) were assessed. The single-factor evaluation method, constant price for GDP, and correlation analyses were adopted. The results showed that: 1) water quality in the YREB improved during the 13th Five-Year Plan period. The number of aquatic environment sections meeting Grade I–III water quality standards increased by 13.1% and the number below Grade V decreased by 2.9%. 2) The values of 12 indicators in the YREB exceeded relevant standards. The indicators with highest concentreation were the total phosphorus, chemical oxygen demand, ammonia nitrogen, and permanganate index, which were relatively high in downstream regions in Anhui Province, Jiangsu Province, and Shanghai Municipality. 3) Ammonia nitrogen, chemical oxygen demand, and total phosphorus emissions per unit area and water extraction per unit area are relatively high in the three downstream regions mentioned above. 4) Increased domestic sewage discharges have increased total wastewater discharges in the YREB. 5) River water quality in the YREB strongly correlated with population, economic, and water resource indices and less strongly correlated with government investment, agriculture, meteorology, energy, and forestry indices. This confirmed the need to decrease wastewater discharges and non-point-source pollutant emissions. The aquatic environment could be improved by taking reasonable measures to control population growth, adjusting the industrial structure to accelerate industrial transformation and increase the proportion of tertiary industries, and investing in technological innovations to protect the environment.

Climate change brings new challenges to the sustainable development of agriculture in the new era. Accurately grasping the patterns of climate change impacts on agricultural systems is crucial for ensuring agricultural sustainability and food security. Taking the Loess Plateau (LP), China as an example, this study used a coupling coordination degree model and spatial autocorrelation analysis to portray the spatial and temporal features of crop-cropland coupling relationship from 2000 to 2020 and explored the impact law of climate change through geographically and temporally weighted regression (GTWR). The results were as follows: 1) the crop-cropland coupling coordination degree of the LP showed a gradual upward trend from 2000 to 2020, forming a spatial pattern with lower values in the central region and higher values in the surrounding areas. 2) There was a positive correlation in the spatial distribution of crop-cropland coupling coordination degree in the LP from 2000 to 2020, and the high value-low value (H-L) and low value-low value (L-L) agglomerations continued to expand eastward, while the spatial and temporal evolution of the high value-high value (H-H) and low value-high value (L-H) agglomerations was not obvious. 3) The impacts of climatic elements on crop-cropland coupling coordination degree in the LP showed strong heterogeneity in time scales. The inhibitory impacts of summer days (SU) and frost days (FD) accounted for a higher proportion, while the annual average temperature (TEM) had both promoting and inhibiting impacts. The impacts proportion and intensity of extreme heavy precipitation day (R25), continuous drought days (CDD), and annual precipitation (PRE) all experienced significant changes. 4) In space, the impacts of SU and FD on the crop-cropland coupling coordination degree varied with latitude and altitude. The adaptability of the LP to R25 gradually strengthened, and the extensions of CDD and increase of PRE led to the increasing inhibition beyond the eastern region of LP, and TEM showed a promoting impact in the Fenwei Plain. As an important grain-producing area in China, the LP should actively deal with the impacts of climate change on the crop-cropland coupling relationship, vigorously safeguard food security, and promote sustainable agricultural development.

The alpine ecosystem has great potential for carbon sequestration. Soil organic carbon (SOC) and total nitrogen (TN) are highly sensitive to climate change, and their dynamics are crucial to revealing the effect of climate change on the structure, function, and services of the ecosystem. However, the spatial distribution and controlling factors of SOC and TN across various soil layers and vegetation types within this unique ecosystem remain inadequately understood. In this study, 256 soil samples in 89 sites were collected from the Three River Headwaters Region (TRHR) in China to investigate SOC and TN and to explore the primary factors affecting their distribution, including soil, vegetation, climate, and geography factors. The results show that SOC and TN contents in 0–20, 20–40, 40–60, and 60–80 cm soil layers are 24.40, 18.03, 14.04, 12.40 g/kg and 2.46, 1.90, 1.51, 1.17 g/kg, respectively; with higher concentrations observed in the southeastern region compared to the northwest of the TRHR. One-way analysis of variance reveals that SOC and TN levels are elevated in the alpine meadow and the alpine shrub relative to the alpine steppe in the 0–60 cm soil layers. The structural equation model explores that soil water content is the main controlling factor affecting the variation of SOC and TN. Moreover, the geography, climate, and vegetation factors notably indirectly affect SOC and TN through soil factors. Therefore, it can effectively improve soil water and nutrient conditions through vegetation restoration, soil improvement, and grazing management, and the change of SOC and TN can be fully understood by establishing monitoring networks to better protect soil carbon and nitrogen.

Changes in river cross-section morphology have decisive influences on the flood discharge and sand transport capacity of rivers; thus, these changes strongly reflect the vitality of a river. In this paper, based on the river cross-section and water and sediment data of two different periods (1974–1987 and 2007–2021), the trend analysis, change-point analysis and sediment rating curve method were used to analyze the change process of river cross-section morphology and its response to streamflow and sediment changes in the main river stream of the Yellow River at the Longmen hydrological station. From 1974 to 1987 (except in 1977), the riverbed experienced siltation, and the riverbed elevation rose. Conversely, from 2007 to 2021, the riverbed experienced scouring, and the riverbed elevation gradually decreased. The cross-section shape changed from rectangular to U-shaped (deeper on the right side) at the Longmen cross-section. The changes in streamflow and sediment processes significantly impacted the evolution of river cross-section. Streamflow (P < 0.05), sediment discharge (P < 0.01), and the sediment load coefficients (P < 0.01) decreased significantly. The relationship between the water depth and sediment load coefficients followed a power function. The decreasing trend in sediment discharge was significantly stronger than that in streamflow. Suspended sediment particles tended to become finer. The sediment rating curve indicates that the sediment supply from upstream decreased while the erosive power in the river channel increased, leading to a gradual decline in riverbed elevation at the Longmen cross-section from 2007 to 2021. These findings help us better understand the impacts of ecological restoration on changes in river streamflow and sediment during river evolution.

Rapid increases in Carbon dioxide (CO2) levels could trigger unpredictable climate change. The assessment of spatiotemporal variation and influencing factors of CO2 concentration are helpful in understanding the source/sink balance and supporting the formulation of climate policy. In this study, Greenhouse Gases Observing Satellite (GOSAT) data were used to explore the variability of CO2 concentrations in China from 2009 to 2020. Meteorological parameters, vegetation cover, and anthropogenic activities were combined to explain the increase in CO2 concentration, using pixel-based correlations and Covariance Based Structural Equation Modeling (CB-SEM) analysis. The results showed that the influence of vertical CO2 transport diminished with altitude, with a distinct inter-annual increase in CO2 concentrations at 17 vertical levels. Spatially, the highest values were observed in East China, whereas the lowest were observed in Northwest China. There were significant seasonal variations in CO2 concentration, with maximum and minimum values in spring (April) and summer (August), respectively. According to the pixel-based correlation analysis, the near-surface CO2 concentration was positively correlated with population (r = 0.99, P < 0.001), Leaf Area Index (LAI, r = 0.95, P < 0.001), emissions (r = 0.91, P < 0.001), temperature (r = 0.60, P < 0.05), precipitation (r = 0.34, P > 0.05), soil water (r = 0.29, P > 0.05), nightlight (r = 0.28, P < 0.05); and negatively correlated with wind speed (r = −0.58, P < 0.05). CB-SEM analysis revealed that LAI was the most important controlling factor explaining CO2 concentration variation (total effect of 0.66), followed by emissions (0.58), temperature (0.45), precipitation (0.30), wind speed (−0.28), and soil water (−0.07). The model explained 93% of the increase in CO2 concentration. Our results provide crucial information on the patterns of CO2 concentrations and their driving mechanisms, which are particularly significant in the context of climate change.

Throughout the contemporary Chinese history of geography, geographical engineering has consistently played a pivotal role as a fundamental scientific activity. It possesses its distinct ontological basis and value orientation, rendering it inseparable from being merely a derivative of geographical science or technology. This paper defines geographical engineering and introduces its development history through the lens of Chinese geographical engineering praxises. Furthermore, it is highlighted the logical and functional consistency between the theory of human-earth system and the praxis of geographical engineering. Six modern cases of geographical engineering projects are presented in detail to demonstrate the points and characteristics of different types of modern geographical engineering. Geographical engineering serves as an engine for promoting integrated geography research, and in response to the challenge posed by fragmented geographies, this paper advocates for an urgent revitalization of geographical engineering. The feasibility of revitalizing geographical engineering is guaranteed because it aligns with China’s national strategies.

The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ. Although previous studies have established a stronger correlation between leaf onset and diurnal temperature than between leaf onset and average temperature, current research on modeling spring phenology based on diurnal temperature indicators remains limited. In this study, we confirmed the start of the growing season (SOS) sensitivity to diurnal temperature and average temperature in boreal forest. The estimation of SOS was carried out by employing K-Nearest Neighbor Regression (KNR-TDN) model, Random Forest Regression (RFR-TDN) model, eXtreme Gradient Boosting (XGB-TDN) model and Light Gradient Boosting Machine model (LightGBM-TDN) driven by diurnal temperature indicators during 1982–2015, and the SOS was projected from 2015 to 2100 based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenario datasets. The sensitivity of boreal forest SOS to daytime temperature is greater than that to average temperature and nighttime temperature. The LightGBM-TDN model perform best across all vegetation types, exhibiting the lowest RMSE and bias compared to the KNR-TDN model, RFR-TDN model and XGB-TDN model. By incorporating diurnal temperature indicators instead of relying only on average temperature indicators to simulate spring phenology, an improvement in the accuracy of the model is achieved. Furthermore, the preseason accumulated daytime temperature, daytime temperature and snow cover end date emerged as significant drivers of the SOS simulation in the study area. The simulation results based on LightGBM-TDN model exhibit a trend of advancing SOS followed by stabilization under future climate scenarios. This study underscores the potential of diurnal temperature indicators as a viable alternative to average temperature indicators in driving spring phenology models, offering a promising new method for simulating spring phenology.

Gross primary production (GPP) is a crucial indicator representing the absorption of atmospheric CO2 by vegetation. At present, the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes and leaf-related biophysical parameter leaf area index (LAI), which are not completely synchronized in seasonality with GPP. In this study, we proposed chlorophyll content-based light use efficiency model (CC-LUE) to improve GPP estimates, as chlorophyll is the direct site of photosynthesis, and only the light absorbed by chlorophyll is used in the photosynthetic process. The CC-LUE model is constructed by establishing a linear correlation between satellite-derived canopy chlorophyll content (Chlcanopy) and FPAR. This method was calibrated and validated utilizing 7-d averaged in-situ GPP data from 14 eddy covariance flux towers covering deciduous broadleaf forest ecosystems across five different climate zones. Results showed a relatively robust seasonal consistency between Chlcanopy with GPP in deciduous broadleaf forests under different climatic conditions. The CC-LUE model explained 88% of the in-situ GPP seasonality for all validation site-year and 56.0% of in-situ GPP variations through the growing season, outperforming the three widely used LUE models (MODIS-GPP algorithm, Vegetation Photosynthesis Model (VPM), and the eddy covariance-light use efficiency model (EC-LUE)). Additionally, the CC-LUE model (RMSE = 0.50 g C/(m2·d)) significantly improved the underestimation of GPP during the growing season in semi-arid region, remarkably decreasing the root mean square error of averaged growing season GPP simulation and in-situ GPP by 75.4%, 73.4%, and 37.5%, compared with MOD17 (RMSE = 2.03 g C/(m2·d)), VPM (RMSE = 1.88 g C/(m2·d)), and EC-LUE (RMSE = 0.80 g C/(m2·d)) model. The chlorophyll-based method proved superior in capturing the seasonal variations of GPP in forest ecosystems, thereby providing the possibility of a more precise depiction of forest seasonal carbon uptake.